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http://dx.doi.org/10.9728/dcs.2011.12.3.279

Real-time Sign Object Detection in Subway station using Rotation-invariant Zernike Moment  

Weon, Sun-Hee (숭실대학교 미디어학과)
Kim, Gye-Young (숭실대학교 컴퓨터학과)
Choi, Hyung-Il (숭실대학교 미디어학과)
Publication Information
Journal of Digital Contents Society / v.12, no.3, 2011 , pp. 279-289 More about this Journal
Abstract
The latest hardware and software techniques are combined to give safe walking guidance and convenient service of realtime walking assistance system for visually impaired person. This system consists of obstacle detection and perception, place recognition, and sign recognition for pedestrian can safely walking to arrive at their destination. In this paper, we exploit the sign object detection system in subway station for sign recognition that one of the important factors of walking assistance system. This paper suggest the adaptive feature map that can be robustly extract the sign object region from complexed environment with light and noise. And recognize a sign using fast zernike moment features which is invariant under translation, rotation and scale of object during walking. We considered three types of signs as arrow, restroom, and exit number and perform the training and recognizing steps through adaboost classifier. The experimental results prove that our method can be suitable and stable for real-time system through yields on the average 87.16% stable detection rate and 20 frame/sec of operation time for three types of signs in 5000 images of sign database.
Keywords
Sign detection; Fast Zernike moment; Adaboost classifier;
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Times Cited By KSCI : 1  (Citation Analysis)
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